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An Adaptive Algorithm for Calculating Crosstalk Error for Blind Source Separation
Volume 31, Issue 2 (2020), pp. 299–312
Pub. online: 6 May 2020      Type: Research Article      Open accessOpen Access
Received
1 November 2018
Accepted
1 February 2020
Published
6 May 2020

Abstract

The crosstalk error is widely used to evaluate the performance of blind source separation. However, it needs to know the global separation matrix in advance, and it is not robust. In order to solve these problems, a new adaptive algorithm for calculating crosstalk error is presented, which calculates the crosstalk error by a cost function of least squares criterion, and the robustness of the crosstalk error is improved by introducing the position information of the maximum value in the global separation matrix. Finally, the method is compared with the conventional RLS algorithms in terms of performance, robustness and convergence rate. Furthermore, its validity is verified by simulation experiments and real world signals experiments.

References

 
Amari, S.I. (1997). Neural learning in structured parameter spaces-natural Riemannian gradient. In: Advances in Neural Information Processing Systems, pp. 127–133.
 
Aroudi, A., Mirkovic, B., De Vos, M., Doclo, S. (2016). Auditory attention decoding with EEG recordings using noisy acoustic reference signals. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 694–698.
 
Basak, J., Amari, S.I. (1999). Blind separation of uniformly distributed signals: a general approach. IEEE Transactions on Neural Networks, 10(5), 1173–1185.
 
Bridwell, D.A., Rachakonda, S., Silva, R.F., Pearlson, G.D., Calhoun, V.D. (2018). Spatiospectral decomposition of multi-subject EEG: evaluating blind source separation algorithms on real and realistic simulated data. Brain Topography, 31(1), 47–61.
 
Choi, S., Cichocki, A. (1997). August. Adaptive blind separation of speech signals: Cocktail party problem. In: International Conference on Speech Processing, pp. 617–622.
 
Cichocki, A., Amari, S.I. (2002). Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, Vol. 1. John Wiley and Sons.
 
El-Sankary, K., Asai, T., Motomura, M., Kuroda, T. (2015). Crosstalk Rejection in 3-D-Stacked Interchip Communication With Blind Source Separation. IEEE Transactions on Circuits and Systems II: Express Briefs, 62(8), 726–730.
 
Grellier, O., Comon, P. (1998). Blind separation of discrete sources. IEEE Signal Processing Letters, 5(8), 212–214.
 
Jiang, X. (2014). Blind signal separation for fault analysis of 400 Hz solid-state power supply. In: 2014 33rd Chinese Control Conference (CCC). IEEE, pp. 3213–3218.
 
Laheld, B., Cardoso, J.F. (1993). Separation adaptative de sources en aveugle. Implantation complexe sans contraintes. In: Actes du XIVeme Colloque GRETSI, pp. 329–332.
 
Li, S., Sejnowski, T.J. (1995). Adaptive separation of mixed broadband sound sources with delays by a beamforming Herault–Jutten network. IEEE Journal of Oceanic Engineering, 20(1), 73–79.
 
Li, Z., Feng, Z.P., Chu, F.L. (2012). A performance index of blind source separation based on improved crosstalk index. Zhendong yu Chongji (Journal of Vibration and Shock), 31(18), 83–88.
 
Macchi, O. (1993). Self-adaptive source separation by direct and recursive networks. In: Proceedings International Conference on Digital Signal Processing (DSP’93) Limasol, Cyprus, pp. 1154–1159.
 
Macchi, O., Moreau, E. (1999). Adaptive unsupervised separation of discrete sources. Signal Processing, 73(1–2), 49–66.
 
Mansour, A., Kawamoto, M., Ohnishi, N. (2002). A survey of the performance indexes of ICA algorithms. In: Proc. IASTED Int. Conf. on Modelling, Identification and Control (MIC), pp. 660–666.
 
Mirzal, A. (2017). NMF versus ICA for blind source separation. Advances in Data Analysis and Classification, 11(1), 25–48.
 
Moreau, E., Macchi, O. (1996). High-order contrasts for self adaptive source separation. International Journal of Adaptive Control and Signal Processing, 10(1), 19–46.
 
Moreau, E., Macchi, O. (1998). Self-adaptive source separation. II. Comparison of the direct, feedback, and mixed linear network. IEEE Transactions on Signal Processing, 46(1), 39–50.
 
Nandakumar, M.M., Bijoy, K.E. (2014). October. Performance evaluation of single channel speech separation using non-negative matrix factorization. In: 2014 National Conference on Communication, Signal Processing and Networking (NCCSN). IEEE, pp. 1–4.
 
Parra, L., Sajda, P. (2003). Blind source separation via generalized eigenvalue decomposition. Journal of Machine Learning Research, 4, 1261–1269.
 
Qian, X., Chunsheng, L., Jinfang, C. (2006). Adaptive algorithm for blind source separation from instantaneous noisy mixtures. Journal of Data Acquisition and Processing, 1–8.
 
Yang, H.H. (1999). Serial updating rule for blind separation derived from the method of scoring. IEEE Transactions on Signal Processing, 47(8), 2279–2285.
 
Yang, H.H., Amari, S.I. (1997). Adaptive online learning algorithms for blind separation: maximum entropy and minimum mutual information. Neural Computation, 9(7), 1457–1482.
 
Yatabe, K., Kitamura, D. (2018). April. Determined blind source separation via proximal splitting algorithm. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 776–780.
 
Zhang, T., Li, L., Zhang, G., Gao, C., Hou, R. (2011). Use estimation of performance index to realize adaptive blind source separation. In: 2011 4th International Congress on Image and Signal Processing (CISP), Vol. 5. IEEE, pp. 2322–2326.
 
Zhu, X.L., Zhang, X.D. (2002). Adaptive RLS algorithm for blind source separation using a natural gradient. IEEE Signal Processing Letters, 9(12), 432–435.

Biographies

Lang Rongling

R. Lang is currently an associate professor in the school of Electronic and Information Engineering in BeiHang University. She got BSc degree and MSc degree in applied mathematics, Northwestern Polytechnical University, P.R. China, in 2002. She received PhD degree in automatic control, Northwestern Polytechnical University, Xi’an, PR China, in 2005. Currently, her research area are data driven GNSS signal monitoring, fault Diagnosis and Fault Prognosis.

Ye Wanyang

W. Ye is currently pursuing his master’s degree in communication and information engineering in Beihang University, Beijing, China. He received his bachelor’s degree in electronics and information engineering from Beihang University, Beijing, China. He is currently doing the research on blind signal processing research.

Zhao Fei

F. Zhao got his master’s degree in communication and information engineering in Beihang University, Beijing, China. He received his bachelor’s degree in communication engineering from China University of Geosciences, Beijing, China. He is now doing the research on jamming suppression in satellite navigation system, blind signal processing and spatial spectrum estimation.

Li Zi
Zilihuaiyin@163.com

Z. Li is currently an associate professor in Faculty of Foreign Languages at Huaiyin Institute of Technology. She got her master degree in translation, PLA University of Foreign Languages, PR China, in 2011 and is now studying for a PhD at School of Public Policy and Management at China University of Mining and Technology, PR China. Currently, her research interests include translation and English education.


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Copyright
© 2020 Vilnius University
Open access article under the CC BY license.

Keywords
blind source separation crosstalk error global separation matrix robustness

Funding
This work was partly supported by Pre-research funds (61405180203), the Open Foundation of Shaanxi Key Laboratory of Integrated and Intelligent Navigation (SKLIIN-20180207, SKLIIN-20180106).

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